Overview

Dataset statistics

Number of variables20
Number of observations8949
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.7 MiB
Average record size in memory197.5 B

Variable types

Numeric19
Categorical1

Alerts

balance is highly overall correlated with balance_freq and 5 other fieldsHigh correlation
balance_freq is highly overall correlated with balance and 2 other fieldsHigh correlation
purchases is highly overall correlated with one_purchases and 5 other fieldsHigh correlation
one_purchases is highly overall correlated with purchases and 2 other fieldsHigh correlation
install_purchases is highly overall correlated with purchases and 3 other fieldsHigh correlation
cash_adv is highly overall correlated with balance and 3 other fieldsHigh correlation
purchases_freq is highly overall correlated with purchases and 3 other fieldsHigh correlation
one_purchases_freq is highly overall correlated with purchases and 3 other fieldsHigh correlation
purchases_install_freq is highly overall correlated with purchases and 3 other fieldsHigh correlation
cash_adv_freq is highly overall correlated with balance and 3 other fieldsHigh correlation
cash_adv_trx is highly overall correlated with balance and 3 other fieldsHigh correlation
purchases_trx is highly overall correlated with purchases and 5 other fieldsHigh correlation
min_pay is highly overall correlated with balance and 2 other fieldsHigh correlation
gross_revenue is highly overall correlated with balance and 5 other fieldsHigh correlation
one_payment is highly overall correlated with one_purchases_freqHigh correlation
id is uniformly distributedUniform
id has unique valuesUnique
purchases has 2043 (22.8%) zerosZeros
one_purchases has 4301 (48.1%) zerosZeros
install_purchases has 3915 (43.7%) zerosZeros
cash_adv has 4628 (51.7%) zerosZeros
purchases_freq has 2042 (22.8%) zerosZeros
one_purchases_freq has 4301 (48.1%) zerosZeros
purchases_install_freq has 3914 (43.7%) zerosZeros
cash_adv_freq has 4628 (51.7%) zerosZeros
cash_adv_trx has 4628 (51.7%) zerosZeros
purchases_trx has 2043 (22.8%) zerosZeros
payments has 240 (2.7%) zerosZeros
min_pay has 313 (3.5%) zerosZeros
prc_full_pay has 5902 (66.0%) zerosZeros

Reproduction

Analysis started2023-02-22 17:05:37.304868
Analysis finished2023-02-22 17:06:37.835209
Duration1 minute and 0.53 seconds
Software versionpandas-profiling vv3.6.2
Download configurationconfig.json

Variables

id
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct8949
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14599.957
Minimum10001
Maximum19190
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size139.8 KiB
2023-02-22T09:06:38.009834image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum10001
5-th percentile10464.2
Q112307
median14598
Q316900
95-th percentile18732.6
Maximum19190
Range9189
Interquartile range (IQR)4593

Descriptive statistics

Standard deviation2651.4422
Coefficient of variation (CV)0.18160617
Kurtosis-1.1994295
Mean14599.957
Median Absolute Deviation (MAD)2297
Skewness-0.00073549296
Sum1.3065502 × 108
Variance7030145.7
MonotonicityStrictly increasing
2023-02-22T09:06:38.296927image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10001 1
 
< 0.1%
16136 1
 
< 0.1%
16130 1
 
< 0.1%
16131 1
 
< 0.1%
16132 1
 
< 0.1%
16133 1
 
< 0.1%
16134 1
 
< 0.1%
16135 1
 
< 0.1%
16137 1
 
< 0.1%
16145 1
 
< 0.1%
Other values (8939) 8939
99.9%
ValueCountFrequency (%)
10001 1
< 0.1%
10002 1
< 0.1%
10003 1
< 0.1%
10004 1
< 0.1%
10005 1
< 0.1%
10006 1
< 0.1%
10007 1
< 0.1%
10008 1
< 0.1%
10009 1
< 0.1%
10010 1
< 0.1%
ValueCountFrequency (%)
19190 1
< 0.1%
19189 1
< 0.1%
19188 1
< 0.1%
19187 1
< 0.1%
19186 1
< 0.1%
19185 1
< 0.1%
19184 1
< 0.1%
19183 1
< 0.1%
19182 1
< 0.1%
19181 1
< 0.1%

balance
Real number (ℝ)

Distinct8870
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1564.6476
Minimum0
Maximum19043.139
Zeros80
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size139.8 KiB
2023-02-22T09:06:38.526691image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8.8139652
Q1128.36578
median873.68028
Q32054.3728
95-th percentile5909.3779
Maximum19043.139
Range19043.139
Interquartile range (IQR)1926.0071

Descriptive statistics

Standard deviation2081.584
Coefficient of variation (CV)1.3303852
Kurtosis7.6740465
Mean1564.6476
Median Absolute Deviation (MAD)800.04525
Skewness2.3932705
Sum14002031
Variance4332992
MonotonicityNot monotonic
2023-02-22T09:06:38.740817image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 80
 
0.9%
40.900749 1
 
< 0.1%
468.851415 1
 
< 0.1%
5058.299635 1
 
< 0.1%
296.905944 1
 
< 0.1%
1084.652647 1
 
< 0.1%
237.198442 1
 
< 0.1%
1636.518315 1
 
< 0.1%
1213.551338 1
 
< 0.1%
252.717781 1
 
< 0.1%
Other values (8860) 8860
99.0%
ValueCountFrequency (%)
0 80
0.9%
0.000199 1
 
< 0.1%
0.001146 1
 
< 0.1%
0.001214 1
 
< 0.1%
0.001289 1
 
< 0.1%
0.004816 1
 
< 0.1%
0.006651 1
 
< 0.1%
0.009684 1
 
< 0.1%
0.01968 1
 
< 0.1%
0.021102 1
 
< 0.1%
ValueCountFrequency (%)
19043.13856 1
< 0.1%
18495.55855 1
< 0.1%
16304.88925 1
< 0.1%
16259.44857 1
< 0.1%
16115.5964 1
< 0.1%
15532.33972 1
< 0.1%
15258.2259 1
< 0.1%
15244.74865 1
< 0.1%
15155.53286 1
< 0.1%
14581.45914 1
< 0.1%

balance_freq
Real number (ℝ)

Distinct43
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.87735013
Minimum0
Maximum1
Zeros80
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size139.8 KiB
2023-02-22T09:06:38.967575image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.272727
Q10.888889
median1
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.111111

Descriptive statistics

Standard deviation0.2367981
Coefficient of variation (CV)0.26990148
Kurtosis3.0976066
Mean0.87735013
Median Absolute Deviation (MAD)0
Skewness-2.0241932
Sum7851.4063
Variance0.05607334
MonotonicityNot monotonic
2023-02-22T09:06:39.167201image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
1 6211
69.4%
0.909091 410
 
4.6%
0.818182 278
 
3.1%
0.727273 223
 
2.5%
0.545455 219
 
2.4%
0.636364 209
 
2.3%
0.454545 172
 
1.9%
0.363636 170
 
1.9%
0.272727 151
 
1.7%
0.181818 146
 
1.6%
Other values (33) 760
 
8.5%
ValueCountFrequency (%)
0 80
0.9%
0.090909 67
0.7%
0.1 8
 
0.1%
0.111111 5
 
0.1%
0.125 9
 
0.1%
0.142857 7
 
0.1%
0.166667 6
 
0.1%
0.181818 146
1.6%
0.2 9
 
0.1%
0.222222 5
 
0.1%
ValueCountFrequency (%)
1 6211
69.4%
0.909091 410
 
4.6%
0.9 55
 
0.6%
0.888889 53
 
0.6%
0.875 57
 
0.6%
0.857143 51
 
0.6%
0.833333 60
 
0.7%
0.818182 278
 
3.1%
0.8 20
 
0.2%
0.777778 22
 
0.2%

purchases
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6203
Distinct (%)69.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1003.3169
Minimum0
Maximum49039.57
Zeros2043
Zeros (%)22.8%
Negative0
Negative (%)0.0%
Memory size139.8 KiB
2023-02-22T09:06:39.376377image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q139.8
median361.49
Q31110.17
95-th percentile3998.764
Maximum49039.57
Range49039.57
Interquartile range (IQR)1070.37

Descriptive statistics

Standard deviation2136.7278
Coefficient of variation (CV)2.1296639
Kurtosis111.37992
Mean1003.3169
Median Absolute Deviation (MAD)361.49
Skewness8.1439693
Sum8978683.3
Variance4565605.9
MonotonicityNot monotonic
2023-02-22T09:06:39.585525image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2043
 
22.8%
45.65 27
 
0.3%
60 16
 
0.2%
150 16
 
0.2%
300 13
 
0.1%
200 13
 
0.1%
100 13
 
0.1%
450 12
 
0.1%
50 10
 
0.1%
600 10
 
0.1%
Other values (6193) 6776
75.7%
ValueCountFrequency (%)
0 2043
22.8%
0.01 4
 
< 0.1%
0.05 1
 
< 0.1%
0.24 1
 
< 0.1%
0.7 1
 
< 0.1%
1 2
 
< 0.1%
1.4 1
 
< 0.1%
2 1
 
< 0.1%
4.44 1
 
< 0.1%
4.8 1
 
< 0.1%
ValueCountFrequency (%)
49039.57 1
< 0.1%
41050.4 1
< 0.1%
40040.71 1
< 0.1%
38902.71 1
< 0.1%
35131.16 1
< 0.1%
32539.78 1
< 0.1%
31299.35 1
< 0.1%
27957.68 1
< 0.1%
27790.42 1
< 0.1%
26784.62 1
< 0.1%

one_purchases
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct4014
Distinct (%)44.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean592.50357
Minimum0
Maximum40761.25
Zeros4301
Zeros (%)48.1%
Negative0
Negative (%)0.0%
Memory size139.8 KiB
2023-02-22T09:06:39.788911image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median38
Q3577.83
95-th percentile2671.528
Maximum40761.25
Range40761.25
Interquartile range (IQR)577.83

Descriptive statistics

Standard deviation1659.9689
Coefficient of variation (CV)2.8016183
Kurtosis164.17206
Mean592.50357
Median Absolute Deviation (MAD)38
Skewness10.044622
Sum5302314.5
Variance2755496.6
MonotonicityNot monotonic
2023-02-22T09:06:40.026940image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4301
48.1%
45.65 46
 
0.5%
50 17
 
0.2%
200 15
 
0.2%
60 13
 
0.1%
100 13
 
0.1%
150 12
 
0.1%
70 12
 
0.1%
1000 12
 
0.1%
250 11
 
0.1%
Other values (4004) 4497
50.3%
ValueCountFrequency (%)
0 4301
48.1%
0.01 7
 
0.1%
0.02 2
 
< 0.1%
0.05 1
 
< 0.1%
0.24 1
 
< 0.1%
0.7 1
 
< 0.1%
1 4
 
< 0.1%
1.4 2
 
< 0.1%
2 1
 
< 0.1%
4.99 1
 
< 0.1%
ValueCountFrequency (%)
40761.25 1
< 0.1%
40624.06 1
< 0.1%
34087.73 1
< 0.1%
33803.84 1
< 0.1%
26547.43 1
< 0.1%
26514.32 1
< 0.1%
25122.77 1
< 0.1%
24543.52 1
< 0.1%
23032.97 1
< 0.1%
22257.39 1
< 0.1%

install_purchases
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct4452
Distinct (%)49.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean411.11358
Minimum0
Maximum22500
Zeros3915
Zeros (%)43.7%
Negative0
Negative (%)0.0%
Memory size139.8 KiB
2023-02-22T09:06:40.256525image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median89
Q3468.65
95-th percentile1750.42
Maximum22500
Range22500
Interquartile range (IQR)468.65

Descriptive statistics

Standard deviation904.37821
Coefficient of variation (CV)2.1998257
Kurtosis96.567168
Mean411.11358
Median Absolute Deviation (MAD)89
Skewness7.2988232
Sum3679055.4
Variance817899.94
MonotonicityNot monotonic
2023-02-22T09:06:40.726160image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3915
43.7%
300 14
 
0.2%
200 14
 
0.2%
100 14
 
0.2%
150 12
 
0.1%
125 11
 
0.1%
75 9
 
0.1%
350 8
 
0.1%
450 8
 
0.1%
500 8
 
0.1%
Other values (4442) 4936
55.2%
ValueCountFrequency (%)
0 3915
43.7%
1.95 1
 
< 0.1%
4.44 1
 
< 0.1%
4.8 1
 
< 0.1%
6.33 1
 
< 0.1%
7.26 1
 
< 0.1%
7.67 1
 
< 0.1%
9.28 1
 
< 0.1%
9.58 1
 
< 0.1%
9.65 1
 
< 0.1%
ValueCountFrequency (%)
22500 1
< 0.1%
15497.19 1
< 0.1%
14686.1 1
< 0.1%
13184.43 1
< 0.1%
12738.47 1
< 0.1%
12560.85 1
< 0.1%
12541 1
< 0.1%
12375 1
< 0.1%
12235.05 1
< 0.1%
12128.94 1
< 0.1%

cash_adv
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct4322
Distinct (%)48.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean978.95962
Minimum0
Maximum47137.212
Zeros4628
Zeros (%)51.7%
Negative0
Negative (%)0.0%
Memory size139.8 KiB
2023-02-22T09:06:40.915471image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31113.8687
95-th percentile4647.894
Maximum47137.212
Range47137.212
Interquartile range (IQR)1113.8687

Descriptive statistics

Standard deviation2097.2643
Coefficient of variation (CV)2.14234
Kurtosis52.894099
Mean978.95962
Median Absolute Deviation (MAD)0
Skewness5.1663234
Sum8760709.6
Variance4398517.7
MonotonicityNot monotonic
2023-02-22T09:06:41.150695image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4628
51.7%
495.425832 1
 
< 0.1%
1486.243293 1
 
< 0.1%
855.232779 1
 
< 0.1%
3767.104707 1
 
< 0.1%
291.608512 1
 
< 0.1%
38.690552 1
 
< 0.1%
521.664369 1
 
< 0.1%
1974.202963 1
 
< 0.1%
2462.100789 1
 
< 0.1%
Other values (4312) 4312
48.2%
ValueCountFrequency (%)
0 4628
51.7%
14.222216 1
 
< 0.1%
18.042768 1
 
< 0.1%
18.117967 1
 
< 0.1%
18.123413 1
 
< 0.1%
18.126683 1
 
< 0.1%
18.149946 1
 
< 0.1%
18.204577 1
 
< 0.1%
18.240626 1
 
< 0.1%
18.280043 1
 
< 0.1%
ValueCountFrequency (%)
47137.21176 1
< 0.1%
29282.10915 1
< 0.1%
27296.48576 1
< 0.1%
26268.69989 1
< 0.1%
26194.04954 1
< 0.1%
23130.82106 1
< 0.1%
22665.7785 1
< 0.1%
21943.84942 1
< 0.1%
20712.67008 1
< 0.1%
20277.33112 1
< 0.1%

purchases_freq
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct47
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.49040534
Minimum0
Maximum1
Zeros2042
Zeros (%)22.8%
Negative0
Negative (%)0.0%
Memory size139.8 KiB
2023-02-22T09:06:41.389343image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.083333
median0.5
Q30.916667
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.833334

Descriptive statistics

Standard deviation0.4013597
Coefficient of variation (CV)0.8184244
Kurtosis-1.6386107
Mean0.49040534
Median Absolute Deviation (MAD)0.416667
Skewness0.059970118
Sum4388.6374
Variance0.16108961
MonotonicityNot monotonic
2023-02-22T09:06:41.623831image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
1 2178
24.3%
0 2042
22.8%
0.083333 677
 
7.6%
0.916667 396
 
4.4%
0.5 395
 
4.4%
0.166667 392
 
4.4%
0.833333 373
 
4.2%
0.333333 367
 
4.1%
0.25 345
 
3.9%
0.583333 316
 
3.5%
Other values (37) 1468
16.4%
ValueCountFrequency (%)
0 2042
22.8%
0.083333 677
 
7.6%
0.090909 43
 
0.5%
0.1 27
 
0.3%
0.111111 18
 
0.2%
0.125 32
 
0.4%
0.142857 26
 
0.3%
0.166667 392
 
4.4%
0.181818 16
 
0.2%
0.2 19
 
0.2%
ValueCountFrequency (%)
1 2178
24.3%
0.916667 396
 
4.4%
0.909091 28
 
0.3%
0.9 24
 
0.3%
0.888889 18
 
0.2%
0.875 26
 
0.3%
0.857143 25
 
0.3%
0.833333 373
 
4.2%
0.818182 21
 
0.2%
0.8 9
 
0.1%

one_purchases_freq
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct47
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.20248031
Minimum0
Maximum1
Zeros4301
Zeros (%)48.1%
Negative0
Negative (%)0.0%
Memory size139.8 KiB
2023-02-22T09:06:41.846114image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.083333
Q30.3
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation0.29834506
Coefficient of variation (CV)1.4734522
Kurtosis1.1613194
Mean0.20248031
Median Absolute Deviation (MAD)0.083333
Skewness1.535453
Sum1811.9963
Variance0.089009773
MonotonicityNot monotonic
2023-02-22T09:06:42.066630image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
0 4301
48.1%
0.083333 1104
 
12.3%
0.166667 592
 
6.6%
1 481
 
5.4%
0.25 418
 
4.7%
0.333333 355
 
4.0%
0.416667 244
 
2.7%
0.5 235
 
2.6%
0.583333 197
 
2.2%
0.666667 167
 
1.9%
Other values (37) 855
 
9.6%
ValueCountFrequency (%)
0 4301
48.1%
0.083333 1104
 
12.3%
0.090909 56
 
0.6%
0.1 39
 
0.4%
0.111111 26
 
0.3%
0.125 41
 
0.5%
0.142857 37
 
0.4%
0.166667 592
 
6.6%
0.181818 34
 
0.4%
0.2 27
 
0.3%
ValueCountFrequency (%)
1 481
5.4%
0.916667 151
 
1.7%
0.909091 4
 
< 0.1%
0.9 1
 
< 0.1%
0.888889 2
 
< 0.1%
0.875 6
 
0.1%
0.857143 1
 
< 0.1%
0.833333 120
 
1.3%
0.818182 10
 
0.1%
0.8 4
 
< 0.1%

purchases_install_freq
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct47
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.36447807
Minimum0
Maximum1
Zeros3914
Zeros (%)43.7%
Negative0
Negative (%)0.0%
Memory size139.8 KiB
2023-02-22T09:06:42.285670image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.166667
Q30.75
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.75

Descriptive statistics

Standard deviation0.39745131
Coefficient of variation (CV)1.090467
Kurtosis-1.3987979
Mean0.36447807
Median Absolute Deviation (MAD)0.166667
Skewness0.50902322
Sum3261.7142
Variance0.15796755
MonotonicityNot monotonic
2023-02-22T09:06:42.549132image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
0 3914
43.7%
1 1331
 
14.9%
0.416667 388
 
4.3%
0.916667 345
 
3.9%
0.833333 311
 
3.5%
0.5 310
 
3.5%
0.166667 305
 
3.4%
0.666667 292
 
3.3%
0.75 291
 
3.3%
0.083333 275
 
3.1%
Other values (37) 1187
 
13.3%
ValueCountFrequency (%)
0 3914
43.7%
0.083333 275
 
3.1%
0.090909 12
 
0.1%
0.1 6
 
0.1%
0.111111 9
 
0.1%
0.125 5
 
0.1%
0.142857 6
 
0.1%
0.166667 305
 
3.4%
0.181818 14
 
0.2%
0.2 9
 
0.1%
ValueCountFrequency (%)
1 1331
14.9%
0.916667 345
 
3.9%
0.909091 25
 
0.3%
0.9 19
 
0.2%
0.888889 28
 
0.3%
0.875 28
 
0.3%
0.857143 30
 
0.3%
0.833333 311
 
3.5%
0.818182 21
 
0.2%
0.8 18
 
0.2%

cash_adv_freq
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct54
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.13514068
Minimum0
Maximum1.5
Zeros4628
Zeros (%)51.7%
Negative0
Negative (%)0.0%
Memory size139.8 KiB
2023-02-22T09:06:42.777563image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.222222
95-th percentile0.583333
Maximum1.5
Range1.5
Interquartile range (IQR)0.222222

Descriptive statistics

Standard deviation0.20013229
Coefficient of variation (CV)1.4809182
Kurtosis3.3341906
Mean0.13514068
Median Absolute Deviation (MAD)0
Skewness1.8286441
Sum1209.3739
Variance0.040052935
MonotonicityNot monotonic
2023-02-22T09:06:42.983355image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4628
51.7%
0.083333 1021
 
11.4%
0.166667 758
 
8.5%
0.25 578
 
6.5%
0.333333 439
 
4.9%
0.416667 273
 
3.1%
0.5 215
 
2.4%
0.583333 142
 
1.6%
0.666667 125
 
1.4%
0.090909 70
 
0.8%
Other values (44) 700
 
7.8%
ValueCountFrequency (%)
0 4628
51.7%
0.083333 1021
 
11.4%
0.090909 70
 
0.8%
0.1 39
 
0.4%
0.111111 29
 
0.3%
0.125 47
 
0.5%
0.142857 49
 
0.5%
0.166667 758
 
8.5%
0.181818 42
 
0.5%
0.2 21
 
0.2%
ValueCountFrequency (%)
1.5 1
 
< 0.1%
1.25 1
 
< 0.1%
1.166667 2
 
< 0.1%
1.142857 1
 
< 0.1%
1.125 1
 
< 0.1%
1.1 1
 
< 0.1%
1.090909 1
 
< 0.1%
1 25
0.3%
0.916667 27
0.3%
0.909091 3
 
< 0.1%

cash_adv_trx
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct65
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2490781
Minimum0
Maximum123
Zeros4628
Zeros (%)51.7%
Negative0
Negative (%)0.0%
Memory size139.8 KiB
2023-02-22T09:06:43.214254image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34
95-th percentile15
Maximum123
Range123
Interquartile range (IQR)4

Descriptive statistics

Standard deviation6.8249867
Coefficient of variation (CV)2.1005918
Kurtosis61.640368
Mean3.2490781
Median Absolute Deviation (MAD)0
Skewness5.7209763
Sum29076
Variance46.580443
MonotonicityNot monotonic
2023-02-22T09:06:43.480750image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4628
51.7%
1 886
 
9.9%
2 620
 
6.9%
3 436
 
4.9%
4 384
 
4.3%
5 308
 
3.4%
6 246
 
2.7%
7 205
 
2.3%
8 171
 
1.9%
10 150
 
1.7%
Other values (55) 915
 
10.2%
ValueCountFrequency (%)
0 4628
51.7%
1 886
 
9.9%
2 620
 
6.9%
3 436
 
4.9%
4 384
 
4.3%
5 308
 
3.4%
6 246
 
2.7%
7 205
 
2.3%
8 171
 
1.9%
9 111
 
1.2%
ValueCountFrequency (%)
123 3
< 0.1%
110 1
 
< 0.1%
107 1
 
< 0.1%
93 1
 
< 0.1%
80 1
 
< 0.1%
71 1
 
< 0.1%
69 1
 
< 0.1%
63 1
 
< 0.1%
62 3
< 0.1%
61 1
 
< 0.1%

purchases_trx
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct173
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.711476
Minimum0
Maximum358
Zeros2043
Zeros (%)22.8%
Negative0
Negative (%)0.0%
Memory size139.8 KiB
2023-02-22T09:06:43.720425image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median7
Q317
95-th percentile57
Maximum358
Range358
Interquartile range (IQR)16

Descriptive statistics

Standard deviation24.858552
Coefficient of variation (CV)1.6897388
Kurtosis34.790599
Mean14.711476
Median Absolute Deviation (MAD)7
Skewness4.6304932
Sum131653
Variance617.94759
MonotonicityNot monotonic
2023-02-22T09:06:43.943405image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2043
22.8%
1 667
 
7.5%
12 570
 
6.4%
2 379
 
4.2%
6 352
 
3.9%
3 314
 
3.5%
4 285
 
3.2%
7 275
 
3.1%
5 267
 
3.0%
8 267
 
3.0%
Other values (163) 3530
39.4%
ValueCountFrequency (%)
0 2043
22.8%
1 667
 
7.5%
2 379
 
4.2%
3 314
 
3.5%
4 285
 
3.2%
5 267
 
3.0%
6 352
 
3.9%
7 275
 
3.1%
8 267
 
3.0%
9 248
 
2.8%
ValueCountFrequency (%)
358 1
< 0.1%
347 1
< 0.1%
344 1
< 0.1%
309 1
< 0.1%
308 1
< 0.1%
298 1
< 0.1%
274 1
< 0.1%
273 1
< 0.1%
254 1
< 0.1%
248 2
< 0.1%

credit_limit
Real number (ℝ)

Distinct205
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4494.4495
Minimum50
Maximum30000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size139.8 KiB
2023-02-22T09:06:44.151301image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile1000
Q11600
median3000
Q36500
95-th percentile12000
Maximum30000
Range29950
Interquartile range (IQR)4900

Descriptive statistics

Standard deviation3638.8157
Coefficient of variation (CV)0.80962435
Kurtosis2.8366559
Mean4494.4495
Median Absolute Deviation (MAD)1800
Skewness1.522464
Sum40220828
Variance13240980
MonotonicityNot monotonic
2023-02-22T09:06:44.347790image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3000 784
 
8.8%
1500 722
 
8.1%
1200 621
 
6.9%
1000 614
 
6.9%
2500 612
 
6.8%
4000 506
 
5.7%
6000 463
 
5.2%
5000 389
 
4.3%
2000 371
 
4.1%
7500 277
 
3.1%
Other values (195) 3590
40.1%
ValueCountFrequency (%)
50 1
 
< 0.1%
150 5
 
0.1%
200 3
 
< 0.1%
300 14
 
0.2%
400 3
 
< 0.1%
450 6
 
0.1%
500 121
1.4%
600 21
 
0.2%
650 1
 
< 0.1%
700 20
 
0.2%
ValueCountFrequency (%)
30000 2
 
< 0.1%
28000 1
 
< 0.1%
25000 1
 
< 0.1%
23000 2
 
< 0.1%
22500 1
 
< 0.1%
22000 1
 
< 0.1%
21500 2
 
< 0.1%
21000 2
 
< 0.1%
20500 1
 
< 0.1%
20000 10
0.1%

payments
Real number (ℝ)

Distinct8710
Distinct (%)97.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1733.3365
Minimum0
Maximum50721.483
Zeros240
Zeros (%)2.7%
Negative0
Negative (%)0.0%
Memory size139.8 KiB
2023-02-22T09:06:44.560101image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile90.11142
Q1383.28285
median857.06271
Q31901.2793
95-th percentile6082.2391
Maximum50721.483
Range50721.483
Interquartile range (IQR)1517.9965

Descriptive statistics

Standard deviation2895.1681
Coefficient of variation (CV)1.6702863
Kurtosis54.767277
Mean1733.3365
Median Absolute Deviation (MAD)581.37563
Skewness5.907465
Sum15511628
Variance8381998.6
MonotonicityNot monotonic
2023-02-22T09:06:44.812134image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 240
 
2.7%
201.802084 1
 
< 0.1%
1825.349955 1
 
< 0.1%
2571.573214 1
 
< 0.1%
1903.279643 1
 
< 0.1%
454.888506 1
 
< 0.1%
956.028747 1
 
< 0.1%
4560.77572 1
 
< 0.1%
398.316441 1
 
< 0.1%
2617.887354 1
 
< 0.1%
Other values (8700) 8700
97.2%
ValueCountFrequency (%)
0 240
2.7%
0.049513 1
 
< 0.1%
0.056466 1
 
< 0.1%
2.389583 1
 
< 0.1%
3.500505 1
 
< 0.1%
4.523555 1
 
< 0.1%
4.841543 1
 
< 0.1%
5.070726 1
 
< 0.1%
9.533313 1
 
< 0.1%
12.773144 1
 
< 0.1%
ValueCountFrequency (%)
50721.48336 1
< 0.1%
46930.59824 1
< 0.1%
40627.59524 1
< 0.1%
39461.9658 1
< 0.1%
39048.59762 1
< 0.1%
36066.75068 1
< 0.1%
35843.62593 1
< 0.1%
34107.07499 1
< 0.1%
33994.72785 1
< 0.1%
33486.31044 1
< 0.1%

min_pay
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8636
Distinct (%)96.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean834.07503
Minimum0
Maximum76406.208
Zeros313
Zeros (%)3.5%
Negative0
Negative (%)0.0%
Memory size139.8 KiB
2023-02-22T09:06:45.139618image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile29.908619
Q1163.02948
median289.6869
Q3788.72161
95-th percentile2719.8615
Maximum76406.208
Range76406.208
Interquartile range (IQR)625.69213

Descriptive statistics

Standard deviation2336.1044
Coefficient of variation (CV)2.8008324
Kurtosis292.33071
Mean834.07503
Median Absolute Deviation (MAD)188.7739
Skewness13.807831
Sum7464137.5
Variance5457383.7
MonotonicityNot monotonic
2023-02-22T09:06:45.409504image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 313
 
3.5%
299.351881 2
 
< 0.1%
150.317143 1
 
< 0.1%
271.528169 1
 
< 0.1%
6404.855484 1
 
< 0.1%
616.862544 1
 
< 0.1%
211.984193 1
 
< 0.1%
324.954747 1
 
< 0.1%
1600.26917 1
 
< 0.1%
277.546713 1
 
< 0.1%
Other values (8626) 8626
96.4%
ValueCountFrequency (%)
0 313
3.5%
0.019163 1
 
< 0.1%
0.037744 1
 
< 0.1%
0.05588 1
 
< 0.1%
0.059481 1
 
< 0.1%
0.117036 1
 
< 0.1%
0.261984 1
 
< 0.1%
0.311953 1
 
< 0.1%
0.319475 1
 
< 0.1%
1.113027 1
 
< 0.1%
ValueCountFrequency (%)
76406.20752 1
< 0.1%
61031.6186 1
< 0.1%
56370.04117 1
< 0.1%
50260.75947 1
< 0.1%
43132.72823 1
< 0.1%
42629.55117 1
< 0.1%
38512.12477 1
< 0.1%
31871.36379 1
< 0.1%
30528.4324 1
< 0.1%
29019.80288 1
< 0.1%

prc_full_pay
Real number (ℝ)

Distinct47
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.15373183
Minimum0
Maximum1
Zeros5902
Zeros (%)66.0%
Negative0
Negative (%)0.0%
Memory size139.8 KiB
2023-02-22T09:06:45.653945image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.142857
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.142857

Descriptive statistics

Standard deviation0.29251103
Coefficient of variation (CV)1.9027357
Kurtosis2.4316588
Mean0.15373183
Median Absolute Deviation (MAD)0
Skewness1.9426414
Sum1375.7461
Variance0.0855627
MonotonicityNot monotonic
2023-02-22T09:06:45.927240image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
0 5902
66.0%
1 488
 
5.5%
0.083333 426
 
4.8%
0.166667 166
 
1.9%
0.5 156
 
1.7%
0.25 156
 
1.7%
0.090909 153
 
1.7%
0.333333 134
 
1.5%
0.1 94
 
1.1%
0.2 83
 
0.9%
Other values (37) 1191
 
13.3%
ValueCountFrequency (%)
0 5902
66.0%
0.083333 426
 
4.8%
0.090909 153
 
1.7%
0.1 94
 
1.1%
0.111111 61
 
0.7%
0.125 52
 
0.6%
0.142857 54
 
0.6%
0.166667 166
 
1.9%
0.181818 75
 
0.8%
0.2 83
 
0.9%
ValueCountFrequency (%)
1 488
5.5%
0.916667 77
 
0.9%
0.909091 19
 
0.2%
0.9 16
 
0.2%
0.888889 12
 
0.1%
0.875 18
 
0.2%
0.857143 12
 
0.1%
0.833333 63
 
0.7%
0.818182 17
 
0.2%
0.8 33
 
0.4%

tenure
Real number (ℝ)

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.517935
Minimum6
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size139.8 KiB
2023-02-22T09:06:46.137362image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile8
Q112
median12
Q312
95-th percentile12
Maximum12
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.3371339
Coefficient of variation (CV)0.11609146
Kurtosis7.7073852
Mean11.517935
Median Absolute Deviation (MAD)0
Skewness-2.9447877
Sum103074
Variance1.7879271
MonotonicityNot monotonic
2023-02-22T09:06:46.311682image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
12 7584
84.7%
11 365
 
4.1%
10 236
 
2.6%
6 203
 
2.3%
8 196
 
2.2%
7 190
 
2.1%
9 175
 
2.0%
ValueCountFrequency (%)
6 203
 
2.3%
7 190
 
2.1%
8 196
 
2.2%
9 175
 
2.0%
10 236
 
2.6%
11 365
 
4.1%
12 7584
84.7%
ValueCountFrequency (%)
12 7584
84.7%
11 365
 
4.1%
10 236
 
2.6%
9 175
 
2.0%
8 196
 
2.2%
7 190
 
2.1%
6 203
 
2.3%

one_payment
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size139.8 KiB
1
4648 
0
4301 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8949
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 4648
51.9%
0 4301
48.1%

Length

2023-02-22T09:06:46.498261image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-22T09:06:46.660286image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1 4648
51.9%
0 4301
48.1%

Most occurring characters

ValueCountFrequency (%)
1 4648
51.9%
0 4301
48.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8949
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 4648
51.9%
0 4301
48.1%

Most occurring scripts

ValueCountFrequency (%)
Common 8949
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 4648
51.9%
0 4301
48.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8949
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 4648
51.9%
0 4301
48.1%

gross_revenue
Real number (ℝ)

Distinct8879
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1594.0164
Minimum0
Maximum19043.139
Zeros71
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size139.8 KiB
2023-02-22T09:06:46.819686image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10.019868
Q1131.54818
median896.71136
Q32097.0295
95-th percentile5979.8678
Maximum19043.139
Range19043.139
Interquartile range (IQR)1965.4814

Descriptive statistics

Standard deviation2113.5397
Coefficient of variation (CV)1.3259209
Kurtosis7.5034663
Mean1594.0164
Median Absolute Deviation (MAD)818.62093
Skewness2.3756077
Sum14264853
Variance4467049.9
MonotonicityNot monotonic
2023-02-22T09:06:47.015673image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 71
 
0.8%
40.900749 1
 
< 0.1%
1215.04375 1
 
< 0.1%
1253.188317 1
 
< 0.1%
5207.364173 1
 
< 0.1%
323.1652653 1
 
< 0.1%
1085.776476 1
 
< 0.1%
237.198442 1
 
< 0.1%
1692.607704 1
 
< 0.1%
473.2675737 1
 
< 0.1%
Other values (8869) 8869
99.1%
ValueCountFrequency (%)
0 71
0.8%
0.000199 1
 
< 0.1%
0.001289 1
 
< 0.1%
0.004816 1
 
< 0.1%
0.009684 1
 
< 0.1%
0.01968 1
 
< 0.1%
0.064811 1
 
< 0.1%
0.065402 1
 
< 0.1%
0.147275 1
 
< 0.1%
0.187069 1
 
< 0.1%
ValueCountFrequency (%)
19043.13856 1
< 0.1%
18495.55855 1
< 0.1%
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Interactions

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2023-02-22T09:06:01.686531image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:06:04.691463image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:06:07.541728image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:06:10.621757image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:06:13.465048image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:06:16.266784image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:06:19.362208image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:06:22.160075image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:06:24.963634image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:06:27.832328image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:06:31.369208image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:06:35.316000image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:05:40.725011image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:05:43.753961image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:05:46.841303image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:05:49.821458image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:05:53.140626image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:05:56.240208image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:05:58.963302image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:06:01.863171image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:06:04.841997image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:06:07.967007image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:06:10.779569image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:06:13.617939image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:06:16.422592image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:06:19.522188image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:06:22.310229image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:06:25.122240image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:06:27.986907image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:06:31.561480image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:06:35.495745image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:05:40.877545image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:05:43.908802image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:05:46.988683image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:05:50.013190image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:05:53.295401image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:05:56.382677image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:05:59.116012image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:06:02.040233image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:06:04.984018image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:06:08.113678image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:06:10.926252image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:06:13.763245image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:06:16.572152image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:06:19.670559image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:06:22.455685image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:06:25.274831image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:06:28.137482image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:06:31.749468image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:06:35.674816image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:05:41.029881image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:05:44.090456image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:05:47.136986image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:05:50.198610image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:05:53.462837image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:05:56.538075image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:05:59.263431image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:06:02.195584image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:06:05.133302image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:06:08.272221image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:06:11.086128image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:06:13.916876image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:06:16.723398image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:06:19.826739image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:06:22.605145image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:06:25.430681image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:06:28.299147image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:06:31.933646image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:06:35.836880image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:05:41.183001image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:05:44.474608image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:05:47.289473image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:05:50.392527image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:05:53.618648image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:05:56.677744image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:05:59.406080image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:06:02.348802image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:06:05.277176image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:06:08.420010image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:06:11.238491image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:06:14.063422image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:06:16.874180image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:06:19.974960image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:06:22.752358image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:06:25.587374image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:06:28.457404image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:06:32.136544image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:06:36.046206image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:05:41.343086image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:05:44.623473image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:05:47.431578image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:05:50.553801image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:05:53.766706image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:05:56.822390image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:05:59.545552image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:06:02.504079image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:06:05.418032image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:06:08.560916image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:06:11.385798image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:06:14.206236image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:06:17.016797image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:06:20.117561image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:06:22.885698image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:06:25.730872image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:06:28.606680image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:06:32.348930image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:06:36.304928image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:05:41.534382image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:05:44.807730image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:05:47.586995image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:05:50.731029image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:05:53.935346image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:05:56.975731image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:05:59.699978image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:06:02.676669image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:06:05.573706image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:06:08.720379image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:06:11.550936image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:06:14.362503image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:06:17.177689image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:06:20.276708image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:06:23.040761image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:06:25.890524image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:06:28.768521image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:06:32.582829image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:06:36.546006image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:05:41.699423image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:05:44.955762image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:05:47.728849image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:05:50.882167image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:05:54.084182image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:05:57.114218image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:05:59.836721image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:06:02.829064image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:06:05.718744image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:06:08.858040image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:06:11.695718image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:06:14.502441image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:06:17.326019image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:06:20.423776image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:06:23.179004image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:06:26.037164image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:06:28.913069image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:06:32.752797image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:06:36.723113image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:05:41.849697image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:05:45.104929image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:05:47.869370image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:05:51.027206image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:05:54.239296image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:05:57.257958image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:05:59.980214image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:06:02.989643image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:06:05.864999image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:06:09.003499image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:06:11.852859image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:06:14.649373image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:06:17.475426image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:06:20.575585image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:06:23.323290image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:06:26.198613image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:06:29.091178image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-22T09:06:32.913178image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2023-02-22T09:06:47.215862image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
idbalancebalance_freqpurchasesone_purchasesinstall_purchasescash_advpurchases_freqone_purchases_freqpurchases_install_freqcash_adv_freqcash_adv_trxpurchases_trxcredit_limitpaymentsmin_payprc_full_paytenuregross_revenueone_payment
id1.000-0.244-0.129-0.119-0.189-0.019-0.050-0.025-0.1830.023-0.024-0.024-0.080-0.378-0.224-0.1880.058-0.174-0.2440.170
balance-0.2441.0000.5450.0060.146-0.0900.566-0.1450.120-0.1440.5440.549-0.0470.3720.4320.882-0.4850.0650.9990.030
balance_freq-0.1290.5451.0000.1480.1350.1270.1370.2020.1590.1520.1770.1760.2020.1060.2070.528-0.1740.2280.5380.110
purchases-0.1190.0060.1481.0000.7510.706-0.3850.7950.6930.606-0.391-0.3840.8850.2610.3950.0150.2380.132-0.0030.167
one_purchases-0.1890.1460.1350.7511.0000.200-0.1850.4240.9520.117-0.179-0.1750.5900.3050.3630.0830.0480.0960.1400.143
install_purchases-0.019-0.0900.1270.7060.2001.000-0.3570.7860.1850.923-0.366-0.3570.7840.1230.238-0.0280.2760.125-0.0980.099
cash_adv-0.0500.5660.137-0.385-0.185-0.3571.000-0.454-0.189-0.3780.9410.952-0.4080.1630.2570.464-0.266-0.1130.5830.033
purchases_freq-0.025-0.1450.2020.7950.4240.786-0.4541.0000.4630.852-0.453-0.4470.9240.1040.172-0.0780.2920.098-0.1550.389
one_purchases_freq-0.1830.1200.1590.6930.9520.185-0.1890.4631.0000.112-0.176-0.1740.6060.2820.3200.0670.0610.0840.1140.761
purchases_install_freq0.023-0.1440.1520.6060.1170.923-0.3780.8520.1121.000-0.382-0.3740.7810.0470.121-0.0640.2590.114-0.1520.085
cash_adv_freq-0.0240.5440.177-0.391-0.179-0.3660.941-0.453-0.176-0.3821.0000.983-0.4070.0880.2030.446-0.287-0.1310.5580.123
cash_adv_trx-0.0240.5490.176-0.384-0.175-0.3570.952-0.447-0.174-0.3740.9831.000-0.3990.0970.2150.459-0.281-0.0990.5640.000
purchases_trx-0.080-0.0470.2020.8850.5900.784-0.4080.9240.6060.781-0.407-0.3991.0000.1900.2840.0000.2530.169-0.0560.266
credit_limit-0.3780.3720.1060.2610.3050.1230.1630.1040.2820.0470.0880.0970.1901.0000.4490.2570.0210.1700.3730.228
payments-0.2240.4320.2070.3950.3630.2380.2570.1720.3200.1210.2030.2150.2840.4491.0000.4210.1870.2050.4360.082
min_pay-0.1880.8820.5280.0150.083-0.0280.464-0.0780.067-0.0640.4460.4590.0000.2570.4211.000-0.4070.1420.8800.033
prc_full_pay0.058-0.485-0.1740.2380.0480.276-0.2660.2920.0610.259-0.287-0.2810.2530.0210.187-0.4071.0000.020-0.4830.020
tenure-0.1740.0650.2280.1320.0960.125-0.1130.0980.0840.114-0.131-0.0990.1690.1700.2050.1420.0201.0000.0620.087
gross_revenue-0.2440.9990.538-0.0030.140-0.0980.583-0.1550.114-0.1520.5580.564-0.0560.3730.4360.880-0.4830.0621.0000.025
one_payment0.1700.0300.1100.1670.1430.0990.0330.3890.7610.0850.1230.0000.2660.2280.0820.0330.0200.0870.0251.000

Missing values

2023-02-22T09:06:37.034825image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-02-22T09:06:37.610498image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

idbalancebalance_freqpurchasesone_purchasesinstall_purchasescash_advpurchases_freqone_purchases_freqpurchases_install_freqcash_adv_freqcash_adv_trxpurchases_trxcredit_limitpaymentsmin_payprc_full_paytenureone_paymentgross_revenue
01000140.9007490.81818295.400.0095.400.0000000.1666670.0000000.0833330.000000021000.0201.802084139.5097870.00000012040.900749
1100023202.4674160.9090910.000.000.006442.9454830.0000000.0000000.0000000.250000407000.04103.0325971072.3402170.2222221203395.755780
2100032495.1488621.000000773.17773.170.000.0000001.0000001.0000000.0000000.0000000127500.0622.066742627.2847870.0000001212495.148862
3100041666.6705420.6363641499.001499.000.00205.7880170.0833330.0833330.0000000.083333117500.00.0000000.0000000.0000001211672.844183
410005817.7143351.00000016.0016.000.000.0000000.0833330.0833330.0000000.000000011200.0678.334763244.7912370.000000121817.714335
5100061809.8287511.0000001333.280.001333.280.0000000.6666670.0000000.5833330.000000081800.01400.0577702407.2460350.0000001201809.828751
610007627.2608061.0000007091.016402.63688.380.0000001.0000001.0000001.0000000.00000006413500.06354.314328198.0658941.000000121627.260806
7100081823.6527431.000000436.200.00436.200.0000001.0000000.0000001.0000000.0000000122300.0679.065082532.0339900.0000001201823.652743
8100091014.9264731.000000861.49661.49200.000.0000000.3333330.0833330.2500000.000000057000.0688.278568311.9634090.0000001211014.926473
910010152.2259750.5454551281.601281.600.000.0000000.1666670.1666670.0000000.0000000311000.01164.770591100.3022620.000000121152.225975
idbalancebalance_freqpurchasesone_purchasesinstall_purchasescash_advpurchases_freqone_purchases_freqpurchases_install_freqcash_adv_freqcash_adv_trxpurchases_trxcredit_limitpaymentsmin_payprc_full_paytenureone_paymentgross_revenue
894019181130.8385541.000000591.240.00591.240.0000001.0000000.0000000.8333330.000000061000.0475.52326282.7713201.0060130.838554
8941191825967.4752700.833333214.550.00214.558555.4093260.8333330.0000000.6666670.6666671359000.0966.202912861.9499060.00606224.137550
89421918340.8297491.000000113.280.00113.280.0000001.0000000.0000000.8333330.000000061000.094.48882886.2831010.256040.829749
8943191845.8717120.50000020.9020.900.000.0000000.1666670.1666670.0000000.00000001500.058.64488343.4737170.00615.871712
894419185193.5717220.8333331012.731012.730.000.0000000.3333330.3333330.0000000.000000024000.00.0000000.0000000.0061193.571722
89451918628.4935171.000000291.120.00291.120.0000001.0000000.0000000.8333330.000000061000.0325.59446248.8863650.506028.493517
89461918719.1832151.000000300.000.00300.000.0000001.0000000.0000000.8333330.000000061000.0275.8613220.0000000.006019.183215
89471918823.3986730.833333144.400.00144.400.0000000.8333330.0000000.6666670.000000051000.081.27077582.4183690.256023.398673
89481918913.4575640.8333330.000.000.0036.5587780.0000000.0000000.0000000.16666720500.052.54995955.7556280.256014.554327
894919190372.7080750.6666671093.251093.250.00127.0400080.6666670.6666670.0000000.3333332231200.063.16540488.2889560.0061376.519275